Proteomics in Cell Division

Identifying Functions of Proteins in Mice With Functional Embedding Features

In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods.

Comparative Proteomics Reveals Evidence of Enhanced EPA Trafficking in a Mutant Strain of Nannochloropsis oculata

The marine microalga Nannochloropsis oculata is a bioproducer of eicosapentaenoic acid (EPA), a fatty acid. EPA is incorporated into monogalactosyldiacylglycerol within N. oculata thylakoid membranes, and there is a biotechnological need to remodel EPA synthesis to maximize production and simplify downstream processing. In this study, random mutagenesis and chemical inhibitor-based selection method were devised to increase EPA production and accessibility for improved extraction. Ethyl methanesulfonate was used as the mutagen with selective pressure achieved by using two enzyme inhibitors of lipid metabolism: cerulenin and galvestine-1. Fatty acid methyl ester analysis of a selected fast-growing mutant strain had a higher percentage of EPA (37.5% of total fatty acids) than the wild-type strain (22.2% total fatty acids), with the highest EPA quantity recorded at 68.5 mg/g dry cell weight, while wild-type cells had 48.6 mg/g dry cell weight. Label-free quantitative proteomics for differential protein expression analysis revealed that the wild-type and mutant strains might have alternative channeling pathways for EPA synthesis. The mutant strain showed potentially improved photosynthetic efficiency, thus synthesizing a higher quantity of membrane lipids and EPA. The EPA synthesis pathways could also have deviated in the mutant, where fatty acid desaturase type 2 (13.7-fold upregulated) and lipid droplet surface protein (LDSP) (34.8-fold upregulated) were expressed significantly higher than in the wild-type strain. This study increases the understanding of EPA trafficking in N. oculata, leading to further strategies that can be implemented to enhance EPA synthesis in marine microalgae.

Identification of protein functions in mouse with a label space partition method

Protein is very important for almost all living creatures because it participates in most complicated and essential biological processes. Determining the functions of given proteins is one of the most essential problems in protein science. Such determination can be conducted through traditional experiments. However, the experimental methods are always time-consuming and of high costs. In recent years, computational methods give useful aids for identification of protein functions. This study presented a new multi-label classifier for identifying functions of mouse proteins. Due to the number of functional types, which were termed as labels in the classification procedure, a label space partition method was employed to divide labels into some partitions. On each partition, a multi-label classifier was constructed. The classifiers based on all partitions were integrated in the proposed classifier. The cross-validation results proved that the proposed classifier was of good performance. Classifiers with label partition were superior to those without label partition or with random label partition.

Lipid Cell Biology: A Focus on Lipids in Cell Division

Cells depend on hugely diverse lipidomes for many functions. The actions and structural integrity of the plasma membrane and most organelles also critically depend on membranes and their lipid components. Despite the biological importance of lipids, our understanding of lipid engagement, especially the roles of lipid hydrophobic alkyl side chains, in key cellular processes is still developing. Emerging research has begun to dissect the importance of lipids in intricate events such as cell division. This review discusses how these structurally diverse biomolecules are spatially and temporally regulated during cell division, with a focus on cytokinesis. We analyze how lipids facilitate changes in cellular morphology during division and how they participate in key signaling events. We identify which cytokinesis proteins are associated with membranes, suggesting lipid interactions. More broadly, we highlight key unaddressed questions in lipid cell biology and techniques, including mass spectrometry, advanced imaging, and chemical biology, which will help us gain insights into the functional roles of lipids.

Comparative phosphoproteomic analysis reveals signaling networks regulating monopolar and bipolar cytokinesis

The successful completion of cytokinesis requires the coordinated activities of diverse cellular components including membranes, cytoskeletal elements and chromosomes that together form partly redundant pathways, depending on the cell type. The biochemical analysis of this process is challenging due to its dynamic and rapid nature. Here, we systematically compared monopolar and bipolar cytokinesis and demonstrated that monopolar cytokinesis is a good surrogate for cytokinesis and it is a well-suited system for global biochemical analysis in mammalian cells. Based on this, we established a phosphoproteomic signature of cytokinesis. More than 10,000 phosphorylation sites were systematically monitored; around 800 of those were up-regulated during cytokinesis. Reconstructing the kinase-substrate interaction network revealed 31 potentially active kinases during cytokinesis. The kinase-substrate network connects proteins between cytoskeleton, membrane and cell cycle machinery. We also found consensus motifs of phosphorylation sites that can serve as biochemical markers specific to cytokinesis. Beyond the kinase-substrate network, our reconstructed signaling network suggests that combination of sumoylation and phosphorylation may regulate monopolar cytokinesis specific signaling pathways. Our analysis provides a systematic approach to the comparison of different cytokinesis types to reveal alternative ways and a global overview, in which conserved genes work together and organize chromatin and cytoplasm during cytokinesis.